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Python Data Mining Quick Start Guide
Python Data Mining Quick Start Guide

Python Data Mining Quick Start Guide: A beginner's guide to extracting valuable insights from your data

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Python Data Mining Quick Start Guide

Basic Terminology and Our End-to-End Example

The philosophy behind a quick-start guide is that the topic at hand is best learned by doing. In this chapter, I will present a quick overview of important vocabulary, concepts, and terminology that you need to get started, and then jump directly into a full end-to-end working example of data mining in Python. Later chapters will flesh out the steps in the working example in more detail.

The following topics will be covered in this chapter:

  • Basic data terminology
  • Basic statistics
  • An end-to-end example of data mining in Python

Basic data terminology

This section is meant to be a quick overview of the terms that you should know before you get started. This list is very streamlined and is not exhaustive. Please refer to the suggested reading in Chapter 1, Data Mining and Getting Started with Python Tools, for wider coverage of domain-specific terminology.

Sample spaces

The sample space is the space that is covered by all the possible outcomes of a measurement. For example, if a feature column in a dataset is populated with the number of days last month that a responder watched television, then the sample space will include all the integers in the {0,1,2...31} set. If a manufacturing tool measures the temperature difference before and after processing...

Basic summary statistics

Practitioners in the field of descriptive analytics use a set of four summary statistics to quickly understand a dataset. With practice, you should be able to strengthen your intuition about each one of these statistical measurements. In fact, it's a great place to start with most problem statements that you will face. The four summary statistics are described as follows:

  • Locations: The location or center of the data; this can be measured by the mean (average), median, or mode. The median is the point of delineation in 50% of the data, and the mode is the most occurring points, or largest part of the distribution.
  • Spread: How the data is spread around the center; this can be measured with standard deviation, which sums the average distance from the mean of each data point, or variance, which is the square of the deviation.
  • Shape: A description...

An end-to-end example of data mining in Python

Let's start with a full end-to-end example demonstrating the topics and strategies covered in the rest of the book. Subsequent chapters will go into further detail on each part of the analytical process. I suggest that you read through this example fully before moving on in the book.

Loading data into memory – viewing and managing with ease using pandas

First, we will need to load data into memory so that Python can interact with it. Pandas will be our data management and manipulation library:

# load data into Pandas
import pandas as pd
df = pd.read_csv("./data/iris.csv")

Let's use some built-in pandas features to do sanity checks on our data load and...

Summary

This chapter covered the basic statistics and data terminology that are required for working in data mining. The final portion of the chapter was dedicated to a full working example, which combined the types of techniques that will be introduced later on in this book. After reading this chapter, you should have a better understanding of the thought processes behind analysis and the common steps taken to address a problem statement that you may encounter in the field. The subsequent chapters will explore each aspect of the example in more depth, with the next chapter focusing on collecting data, loading it into memory, and exploring it with ease.

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Key benefits

  • Grasp the basics of data loading, cleaning, analysis, and visualization
  • Use the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data mining
  • Your one-stop guide to build efficient data mining pipelines without going into too much theory

Description

Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle.

Who is this book for?

Python developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started.

What you will learn

  • Explore the methods for summarizing datasets and visualizing/plotting data
  • Collect and format data for analytical work
  • Assign data points into groups and visualize clustering patterns
  • Learn how to predict continuous and categorical outputs for data
  • Clean, filter noise from, and reduce the dimensions of data
  • Serialize a data processing model using scikit-learn's pipeline feature
  • Deploy the data processing model using Python's pickle module

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Publication date : Apr 25, 2019
Length: 188 pages
Edition : 1st
Language : English
ISBN-13 : 9781789800265
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Product Details

Publication date : Apr 25, 2019
Length: 188 pages
Edition : 1st
Language : English
ISBN-13 : 9781789800265
Category :
Languages :
Concepts :
Tools :

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Table of Contents

8 Chapters
Data Mining and Getting Started with Python Tools Chevron down icon Chevron up icon
Basic Terminology and Our End-to-End Example Chevron down icon Chevron up icon
Collecting, Exploring, and Visualizing Data Chevron down icon Chevron up icon
Cleaning and Readying Data for Analysis Chevron down icon Chevron up icon
Grouping and Clustering Data Chevron down icon Chevron up icon
Prediction with Regression and Classification Chevron down icon Chevron up icon
Advanced Topics - Building a Data Processing Pipeline and Deploying It Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Amazon Customer Jun 23, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great explanation and presentation covered with examples on all topics related to data mining and machine learning principles. Recommend to anyone starting in this field as well as seasoned professionals.
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Colleen Green Jul 19, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Novice going into this, couldn’t imagine a more user-friendly introduction to data mining!! The author incorporates visuals and clarity of writing I found helpful. I know friends who are learning Python and I always recommend this in case data mining is something they see in their future.
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Keegan Schlake Jun 13, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a wonderful introduction to data mining and was incredibly helpful. The data sets and applications come at no extra charge and the book is both intuitive and well paced. I could not give this book a higher recommendation if you're interested in the field.
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TJMOTOX5 Jun 10, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very good intro book to get you started. The author is positive and upbeat and tries to keep the material interesting. The coding exercises are easy to follow and the concepts are clearly explained. The chapter on clustering is the best description I’ve ever seen on the topic. It’s a bit short, but the price is right!
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Carlos Vicens Jun 07, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great intro to the field. I started with no background and got some code going in no time. The second half of the book is conceptual. I think it will be very helpful in my new career to have seen that first principles treatment of clustering and prediction algos. I recommend to anyone trying to get started in the field of data mining or machine learning.
Amazon Verified review Amazon
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